Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling

Share/Save/Bookmark

Lith van, Pascal and Betlem, Ben and Roffel, Brian (2002) Fuzzy Clustering, Genetic Algorithms and Neuro-Fuzzy Methods Compared for Hybrid Fuzzy-First Principles Modeling. Systems Analysis Modelling Simulation, 42 (4). pp. 597-630. ISSN 0232-9298

[img]PDF
Restricted to UT campus only
: Request a copy
323Kb
Abstract:Hybrid fuzzy-first principles models can be a good alternative if a complete physical model is difficult to derive. These hybrid models consist of a framework of dynamic mass and energy balances, supplemented by fuzzy submodels describing additional equations, such as mass transformation and transfer rates. Identification of these fuzzy submodels is one of the main issues in constructing hybrid models. In this paper, a new approach to constructing hybrid fuzzy-first principles models is presented, which uses a Kalman filter for parameter estimation. In addition, a comparison between three classes of identification techniques for fuzzy submodels is presented: fuzzy clustering, genetic algorithms and neuro-fuzzy methods. The comparison is illustrated for a penicillin fed batch-reactor test case. Fuzzy clustering proved to be the most suitable technique, with genetic algorithms being a good alternative.
Item Type:Article
Copyright:Taylor & Francis
Faculty:
Science and Technology (TNW)
Research Group:
Link to this item:http://purl.utwente.nl/publications/61391
Official URL:http://dx.doi.org/10.1080/02329290290031350
Export this item as:BibTeX
EndNote
HTML Citation
Reference Manager

 

Repository Staff Only: item control page

Metis ID: 211411